Improving Distributional Regression Forests for Post-processing Extreme Wind Gust Forecasts

Extreme events, such as wind gusts or extreme precipitation, can generate huge impacts on our society. Accurate predictions of such events are thus vital for taking preventive measures. In spite of continued scientific progress in weather forecasting, ensemble forecasts exhibit biases and under-dispersion and have to be calibrated using observations before being used, e.g., in hydrological or renewable energy applications. Several post-processing techniques have therefore been developed and applied over the last decades in order to improve forecast quality. Many existing post-processing methods are parametric, i.e. they assume that the predictive distribution belongs to a class of known probability distributions. Parameters of the assumed distribution are then modeled as functions of predictors obtained from numerical weather prediction models, for example using nonhomogeneous regression or more advanced tree-based methods. One of the main limitations of such methods is that a suitable family of probability distributions has to be selected to describe the distribution of the target variable. This implies that intermediate and high values are modeled with the same parametric distribution, which can lead to suboptimal results for extremes. We propose to adapt an existing distributional tree-based technique (Distributional Regression Forests) used for ensemble post-processing to overcome this limitation by allowing the method to choose different statistical distributions to model intermediate and extreme values. The proposed method is applied to forecasts of 6-hourly maximum wind gusts from 2018 to 2022 over the Netherlands using the ECMWF-IFS ensemble data. Results are compared against several state-of-the-art parametric and non-parametric post-processing methods. In comparison with these alternatives, the proposed algorithm reasonably corrects intermediate values and presents the largest skill improvements for wind gust extremes depending on lead times, stations and thresholds. However, it remains difficult to beat the raw forecasts of extremes. Therefore, it encourages further research on adding more flexibility to parametric methods for the post-processing of extreme weather forecasts.